LEE- Proje ve Yapım Yönetimi-Yüksek Lisans
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ÖgeComparison of the architectural design process quality between BIM and traditional design methods(Graduate School, 2022)Quality in the construction industry requires meeting the requirements and needs of the designers, engineers, and contractors involved in the process, as well as satisfying the customer's expectations. Although quality standards and quality improvement policies are not as established as in the manufacturing industries, it is important for organizations in the construction industry to gain a competitive advantage by ensuring high quality and low costs. When considering construction quality, quality control of the productions and resources that take place during construction usually comes to mind. However, this approach does not include investigating and improving the root causes of quality problems arising from the planning and design processes. In addition, the fact that the control and improvement of quality problems that arise in construction projects are practiced during the construction phase prevents the quality problems derived from design and engineering errors to be solved at the design stage with lower costs and resources. There are also instances where a design defect cannot be corrected in construction at reasonable costs. For this reason, the understanding of quality in construction should be approached in two ways. First of all, product quality in construction is related to the effectiveness of materials, equipment and technologies used in the construction phase. Secondly, process quality in the construction industry is related to the organization and management methods applied during the design, construction and operation phases. The expectations of the stakeholders (i.e., building owner, designers, engineers and management and organization) involved in the architectural design process are different from each other, and therefore they consider different factors when evaluating the design process quality. In order for all stakeholders to develop a common understanding of quality, effective communication methods between stakeholders should be determined, feedback systems should be established, and all parties should be involved in the process from the very beginning of the project design. As a result, the design output, which is expressed in clear and effective project documentation, developed by receiving feedbacks and checked for constructability, ensures that the product reaches the expected quality. In traditional design methods, the design process is divided into sub-processes and the complex design process is tried to be facilitated. However, this division causes the design process to progress independently between disciplines and causes separation between design teams, creating problems in communication, collaboration and integration, which are very important for achieving design quality. Therefore, the adaptation of digitalization, integration and collaboration tools in the design process has the potential to provide solutions to the quality problems present in the design processes. The Building Information Modeling (BIM) method, which has been discussed since the 2000s to provide solutions to these problems, can be used as a tool to improve the quality of the architectural design process with its features such as involving all stakeholders in the design process, providing interdisciplinary integration and supporting information sharing. In this study, the effect of BIM adaptation on the factors affecting quality in the architectural design process is compared with traditional design processes, to conclude whether the use of BIM is a suitable method to improve the quality of the architectural design process. Within the scope of the study, it was decided to conduct a survey as a research method, with the anticipation that there will be a difference between the evaluation of the design process quality of the architects who use traditional design processes and BIM processes in current architectural design applications. First, by conducting a comprehensive literature research, architectural design process is defined in a structured manner and the differences between traditional and integrated design processes were determined. Then, BIM process was examined; the usage areas, maturity levels and benefits of BIM to the design process were explained. Finally, the factors that determine the quality in the design process were determined and the quality evaluation scales for the survey study were prepared by comparing these factors with the benefits of BIM. In order to evaluate the difference between traditional architectural design processes and BIM processes in the context of quality management concepts, these scales were determined as: (1) design requirements, (2) communication (3) drawing and specification control, (4) tools, methods and techniques, (5 ) design validation, (6) project team, and (7) management and organization. In the analysis of the data obtained from a total of 83 participants as a result of the survey conducted with BIM and traditional design process users, the focus was on the participants' evaluation of the quality factors in the architectural design processes and the relationship of these factors with the design process quality. The data obtained through the survey from the evaluations of 42 users of BIM and 41 traditional design processes, were analyzed with descriptive and statistical analysis methods. Before the analysis of the data, reliability analysis was performed on the scales and it was determined that the scales used in the questionnaire were reliable. To determine the relationships between the scales the correlation analysis is applied, the relationships between the factors affecting the quality of the architectural design process of the participants presents conformity with the findings of the literature review. For the comparison of the participant groups, descriptive analysis was first applied, the mean values of the evaluations were presented, and it was observed that there was a difference in the evaluation of the quality scales between the two groups. In order to determine the statistically significant relationships between these differences, t-test analysis was performed for independent samples. The results of the independent samples t-test analysis have presented statistical significance, which demonstrates that the architectural design process quality of the participants using BIM processes is higher than the traditional design processes. In particular, significant differences were seen between the two groups in the scales of tools, methods and techniques (μd = 2,14), design verification (μd = 1,97), drawing and specification control (μd = 1,91), communication (μd = 1,85), management and organization (μd = 1,20), and design requirements(μd = 1,15). It was observed that this difference was less effective at the scale of the project team (μd = 0,47). Within the scope of this study, these results indicate that the use of BIM improves the quality of the architectural design process.
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Ögeİnşaat sektöründe çalışanların bakış açısından yapım projelerinde bilgi israfı(Lisansüstü Eğitim Enstitüsü, 2023)İnşaat sektörü örtülü bilginin yoğun olarak üretildiği bir sektördür. Örtülü bilginin aktarılması, paylaşılması veya depolanması zordur. Bir firma, örtülü bilgiyi herkesin anlayabileceği ve erişebileceği hale, yani açık bilgiye dönüştürdüğü zaman bilgi, firma içerisinde tekrar kullanılabilecek ve organizasyon için stratejik değere dönüşecektir, dolayısıyla kaybolmayacaktır. İnşaat projelerinin çok paydaşlı olması, her projede farklı uzmanlık alanlarından ekiplerin yer alması, ekiplerin sürekli değişmesi ve üretim sürecinin uzun olması gibi nedenlerden ötürü örtülü bilginin açık bilgiye dönüşümünde bazı aksaklıklar yaşanabilmektedir. Firmalar sahip oldukları entelektüel sermayeden yararlanma konusunda başarısız olmakta ve çalışanların sahip oldukları örtülü bilginin firmaya değer üretecek şekle sokulması güçleşebilmektedir. Bu durum firmanın bilgi israfı riskiyle karşı karşıya kalması demektir. Bilgi israfı, organizasyon içerisinde erişilebilir düzeyde olsa da, değer üretmek veya müşterinin ihtiyaçlarını karşılamak için kullanılmayan bilgiyi ifade etmektedir. Çalışan ve çalışanın sahip olduğu bilgi hala örgüt içerisindedir fakat örgütsel sistemde sahip oldukları bilginin keşfedilmesine, kullanılmasına ve uygulanmasına izin vermeyen sorunlar veya verimsizliklerle karşılaşılmıştır. Literatürde bu verimsizliklere, bilginin örtülü bilgiden açık bilgiye dönüşümünde ve bilgi yönetimi sürecinde yaşanan aksaklıklar örnek gösterilmektedir. Bu tez çalışmasının amacı inşaat sektöründe bilgi israfının oluşumuna sebep olan durumları çözümlemek, bu israf türüne dair farkındalık düzeyini tespit etmek, inşaat firmalarında bilgi israfının yönetimine veya önlenmesine dair uygulamaları, eksiklikleri, ihtiyaçları ve önerileri belirlemektir. Toplamda altı bölümden oluşan tezin birinci bölümünde literatür özetiyle çalışmaya giriş yapılmış, çalışmanın amacı ve yöntemi açıklanmıştır. İkinci bölümde çalışmanın kuramsal çerçevesini oluşturan literatür taraması verilmiştir. Üçüncü bölümde literatürde güncel bir çalışma alanı olan ve bilgi israfı kavramının arka planını oluşturan bilgi riskleri konusu ele alınmış ve literatürde bahsi geçen bilgi israfı çeşitlerine detaylarıyla birlikte yer verilmiştir. Tezin beşinci bölümünde saha çalışmasında elde edilen bulgular çizelge ve şekillerle sunulmuş, yorumlanmış ve tartışmaya açılmıştır. Altıncı bölümde çalışmanın özetine ve inşaat firmalarının bilgi israfının önlenmesinde uygulayabileceği yöntemlere dair önerilere yer verilmiştir.
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ÖgeArtificial intelligence influence for digitalized construction project management during planning phase(Graduate School, 2024-11-12)Digitalization has become mandatory considering the efficiency and productivity criteria reached in the 21st century and the developments in the construction industry in the last decade. Considering the size and employment rate it occupies in the world economy, the revolutionary digitalization adventure, which continues but has a long way to go, is far from being completed. The most valuable potential of Artificial Intelligence (AI) is that all industry components that play a role in the project life cycle can understand and benefit from it, even at different levels. AI is a set of sciences, theories and techniques whose purpose is to reproduce the complex tasks that a human can perform and the cognitive abilities of a human done by a machine. Additionally, these systems can process data and information like intelligent behavior, often including reasoning, learning, perception, prediction, planning or control elements. However, like many technologies in the construction industry, where the digital revolution is not completed, AI has not yet been fully adopted. In the construction industry, which differs negatively from other industries by starting digitalization late, digitalized practices can be observed at every stage of the project life cycle. Still, they cannot be significantly differentiated at any stage. However, various studies have noticed the greatest potential hidden in the planning phase. For this reason, this thesis aims to convey valuable information about AI to the sector components, shed light on the digital project and construction management planning phase from AI's perspective, contribute to its adaptation. (1) "What are the characteristics of Artificial Intelligence technologies in the construction industry?", (2) "What are the functions and applications of Artificial Intelligence in the construction industry?", (3) "What are the key criteria to be considered for the performance and impact of adopting Artificial Intelligence?", ( 4) "To what extent and how can Artificial Intelligence, subsets and technologies support the management of the construction project planning phase?"; Answers to these questions were sought to achieve the aims of this research. When the project construction literature is examined comprehensively, although the potential of AI technologies in the project planning phase is revealed, a lack of information and research on digital adaptation is noticed. Research conducted in the search engines of various databases focused on studies on "artificial intelligence features and subfields", "artificial intelligence applications", "relationship between artificial intelligence and the construction industry", and "artificial intelligence applications in the planning stage". As a result of the literature review, six major artificial intelligence-supported services that have the potential to play an active role in digitalized project planning in the construction industry were identified. These influence areas of AI are automated project scheduling, labor/productivity management, predictive modeling and risk determination, health and safety, accessible data and cost engineering. In the next step, a survey was conducted to various professionals from the construction industry to investigate the impact of using AI for digital construction project management during the planning phase. It was aimed to reach the effects of the concept of AI on project and construction management at the planning phase with the answers received to the questions asked on a 7-point Likert scale (1=not effective, 7=extremely effective) in the first part of the survey, which consists of two parts. In the second part, questions were asked to obtain demographic information about the survey participants. The aim of the survey study was to reach a 50% response rate, which was planned to be shared with 120 participants from the construction industry, determined by purposive sampling. The data obtained from the survey conducted using the SPSS 27 program was analyzed by subjecting it to various tests. First, Cronbach's alpha (α) reliability test was applied due to the need to test data reliability. Then, which of the various parametric tests would be used in the advanced statistical analyses planned to be carried out within the scope of the study is evaluated depending on the normality values of the Skewness-Kurtosis test. Later, the obtained data were subjected to the Pearson correlation test, which revealed the relationships between the evaluation criteria presented to the participants in the first part of the survey. Then, depending on the number of groups, independent samples t-test and ANOVA test were applied to evaluate the variables of the relevant groups, in order to search whether there is statistically significant difference in this evaluation. The t-test was applied to two paired groups. These groups are designed as (1) professions, (2) users and non-users of AI applications. During the one-way ANOVA test, the results divided into more than two groups were analyzed whether there is a significant difference in variable mean values. ANOVA groups are designed as (1) experience in the AECO industry, (2) associated organization, (3) organization scale and (4) years of experience with AI and concepts. Finally, Levene and post-hoc tests were applied to complete this phase of study. In scaling the effect of the specified AI applications on the variables, all influence areas have high mean scores. In the planning phase of construction projects, the highest effect was observed in accessible data, while the lowest was observed in health and safety. It was also understood that many influence areas were highly correlated. To sum up, within the scope of this thesis study, the concept of AI, its features, and current and future applications among digital technologies in the construction industry have been examined and analyzed. It aims to contribute to the limited literature, especially considering its position in the planning phase from the project and construction management perspective. In addition, many AI tools, applications and contributions that can be used during the construction project management and planning phase are mentioned. Also, the results and potentials that will arise from its practical use are mentioned. In this way, it can be claimed that the use of AI for digitalized project planning has the potential to solve low productivity and inefficiency problems, the biggest problems that the construction industry has had difficulty overcoming for decades. Finally, it is intended that this completed study will be a source of inspiration for industry-component companies, institutions, individuals and especially researchers who need motivation for the digitalization and progress of the construction industry.
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ÖgeLeveraging ai in construction management(Graduate School, 2024-06-07)A 2020 McKinsey analysis shows that the construction sector accounts for 13% of the global GDP. The sector affects economic growth and quality of life through its real estate, infrastructure, and industrial projects. Roughly 7.22% of the world's workforce works there. Nevertheless, the industry faces problems with labour productivity, which result in material and financial waste and generate delays, as well as cost overruns. Digital technologies like AI, BIM, and IoT have the potential to increase production and efficiency greatly, but the industry is adopting them slowly. Despite the difficulties, the demand for environmentally friendly building practices and the effects of recent occurrences such as the COVID-19 pandemic highlights how urgently digital transformation is required. Artificial Intelligence (AI) has a significant opportunity to improve decision-making and operational efficiency, potentially increasing industry output by $1.6 trillion annually. This thesis aims to investigate the incorporation of Artificial Intelligence (AI) in construction management to improve efficiency and tackle ongoing problems within the industry. Poor labour productivity and reluctance to digital change are problems facing the construction industry, which is vital to the global economy and employment. The research has been divided into two sections: a thorough literature review on artificial intelligence in construction management and a thorough analysis using bibliometric, text-mining, and content analysis tools. Through the lens of artificial intelligence (AI) technologies, the research focuses on the adoption and impact of digitalization on construction project management. The introduction, a thorough literature review, an explanation of the research methods in detail, a discussion of the findings, and a final chapter summarizing conclusions and suggestions comprise the five chapters that make up the thesis. The literature review dives into the development of AI and how it can be used from a management standpoint in the construction industry. It investigates the industry's innovation and digitisation trends, emphasising how slowly adoption is happening compared to other industries. The evaluation notes the increasing importance of AI in a range of construction-related operations. It emphasises the need for a more creative strategy to overcome the industry's historical resistance to change. This part of study examines artificial intelligence's (AI) involvement in the construction industry, focusing on how it developed, affected construction management, and how it was used throughout the lifecycles of projects. Its initial goal is to comprehend construction management; after that, it investigates how it relates to innovation and investigates artificial intelligence and cutting-edge technologies. The final section of the literature research explores the connection between artificial intelligence (AI) and construction project management, emphasising the potential applications of AI in the construction industry. The construction sector is gradually embracing digital technology, including artificial intelligence (AI), despite obstacles like an excessive dependence on manual labour and the unique nature of each project. Although the industry's inherent fragmentation and old procedures slow this transformation, technological developments have the potential to increase production and efficiency dramatically. AI is predicted to transform construction management by making it possible to execute projects more meticulously and effectively. To increase the efficiency and sustainability of the construction sector, future research will concentrate on integrating AI into the processes more thoroughly, overcoming obstacles like cost, cultural resistance, and technological difficulties. The review highlights AI's vital role in advancing industry standards and meeting complicated project needs, outlining its enormous potential and current limitations in construction management. AI has the potential to transform construction management significantly, but broad adoption and achieving the most achievable benefit still depend on overcoming enormous implementation challenges. A mixed-methods approach comprising content analysis, text mining, bibliometric analysis, and a comprehensive literature review is described in the methodology section. Identifyingkey research themes, patterns, and gaps in the literature facilitates a comprehensive understanding of the state of artificial intelligence (AI) in construction management. Bibliometric data analysis shows a growing trend in AI research in the construction industry, particularly after 2019, which suggests that people are becoming more aware of AI's potential. The most prominent journals in the discipline are addressed, together with yearly trends in article publication and citation analysis, to determine key publications. To investigate the relationship between collaboration and impact, the study analyzes important countries and organizations. The field's most important research subjects and hotspots were determined by using keyword co-occurrence analysis. While text-mining and content analysis provide a greater understanding of popular topics and the field's conceptual framework, bibliometric analysis highlights important research themes and trends. The text mining analysis carried out to identify important research topics in artificial intelligence applications for construction management is covered in the second section of the data analysis. This research was conducted with the help of keyword co-occurrence data. VOSviewer was used to evaluate the titles and abstracts of 71 papers. A network visualization illustrates the connections and frequencies of term interactions found through the co-occurrence analysis. The image demonstrated the phrases' interrelation with node size reflecting keyword frequency, line thickness indicating association strength, and node closeness indicating strong linkages,. The two primary areas of research themes were "Artificial Intelligence Applications" and "Construction Management." This classification helped to examine the use of AI in construction management in-depth by organizing the findings into coherent clusters. The investigation revealed various AI applications that are crucial for construction management: Building Information Modeling (BIM), deep learning, machine learning, support vector machines (SVM), fuzzy logic, genetic algorithms, neural networks, automation, and computer simulation were among the applications of artificial intelligence. Practical factors such as construction engineering, project management, scheduling and planning, cost estimation, and resource allocation are the main focus of construction management. To demonstrate the frequency and overall link strength of each AI application, each category was further examined. This revealed the applications' prominence and connectivity within the field of study. The study emphasized the direct relationship between AI applications and concepts like productivity and optimization across the industry. The text's accompanying figures and tables gave readers a thorough understanding of the AI technologies leading the industry and highlighted their widespread adoption and relevance in today's corpus of research. The final section of data analysis applied in this study is content analysis, utilizing qualitative data from academic publications to analyse research objectives related to creating a status-quo understanding of the area. The main goal is to investigate studies how the applications of artificial intelligence (AI) and construction management. To find scholarly works relevant to AI applications in construction management, a thorough search was carried out. To find relevant information, the search utilized key terms refined by text mining. An extensive annotation procedure followed discussions of different AI applications, highlighting examples that went beyond the initial search parameters. Subjects related to construction management were used to identify these articles. These applications improve decision-making, provide predictive analytics, and raise the project's general efficiency and safety. Cost management, project management, time management, and construction engineering are just a few areas in which artificial intelligence (AI) technologies significantly improve construction management. When AI approaches are included, construction projects benefit from increased accuracy, efficiency, and decision-making . The analysis shows how AI can completely transform the construction sector and offers a path forward for future developments. In the research study, robotics, computer vision, and machine learning are the three leading AI technologies revolutionising construction management. These technologies improve decision-making, provide predictive analytics, and raise the project's general efficiency and safety. The thesis acknowledges the difficulties of using AI in the construction industry, such as the lack of skilled labour, cultural opposition, and infrastructure limitations. LEVERAGING AI IN CONSTRUCTION MANAGEMENT SUMMARY LEVERAGING AI IN CONSTRUCTION MANAGEMENT SUMMARY The thesis concludes by highlighting the importance of adopting AI and digital solutions in the construction sector. It offers tactics for raising digital literacy, encouraging creativity, and creating laws that will encourage the use of AI. The real-world outcomes affect researchers, legislators, and business professionals alike, promoting cooperation, the creation of new laws, and additional research into the revolutionary possibilities of artificial intelligence. Future studies will focus on ethical issues, the integration of AI with other developing technologies, and the long-term effects of AI on occupational roles and skills. The thesis highlights the possibilities for higher efficiency, innovation, and a more resilient and sustainable economy, and it calls for a proactive strategy to harness AI in the construction sector.
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ÖgeTürk inşaat sektöründe dijital dönüşüm stratejileri(Lisansüstü Eğitim Enstitüsü, 2024-06-11)Tarih boyunca medeniyetlerin mirasını taşıyan inşaat sektörü sadece yapılı çevreyi inşa etmekle kalmamış, ülke ekonomisinin ivmesini belirleyen önemli bir mihenk taşı olmuştur. İnşaat sektörü malzeme üretiminden lojistik hizmetlerine, mühendislik uygulamalarından peyzaj düzenlemelerine kadar yan sanayileri ve hizmet sektörlerini besleyip geniş bir istihdam ağı oluşturur. Bu geniş etki alanı, inşaat sektörünün ekonomik kalkınmada ve istihdam yaratmada kritik bir rol oynadığını gözler önüne sermektedir. Yerel ve küresel ekonomilerde önemli bir unsur olmasına rağmen inşaat sektörü genellikle verimsiz üretim süreçlerine sahip olmasıyla eleştirilir. Zaman ve maliyet aşımları, kaynakların etkin bir şekilde kullanılamaması, proje ekipleri ve paydaşlar arası iletişim sorunları ve iş süreçlerindeki karmaşıklık çoğu inşaat projesinde sürekli tekrar eden sorunlardır. Bu sorunlar, inşaat projelerinin tamamlanma sürecini uzatarak maliyetlerin artmasına ve kaynakların israfına neden olmaktadır. Endüstri 4.0 olarak adlandırılan dijital çağ ile birlikte bu sorunların üstesinden gelmek için birçok yeni teknoloji tanıtılmış; akıllı makineler ve sensörler üretim süreçlerinde verimliliğin artmasında aktif rol oynamaya başlamıştır. Nesnelerin interneti (IoT), büyük veri, otonom robotlar, simülasyon, katmanlı üretim gibi teknolojiler Endüstri 4.0 ile birlikte yaygınlaşmıştır. Bu teknolojiler, üretim süreçlerini optimize ederek maliyetleri düşürmekte ve projelerin daha hızlı tamamlanmasını sağlamaktadır. Örneğin, sensörler ve IoT cihazları sayesinde, inşaat sahalarındaki ekipman ve malzeme kullanımı gerçek zamanlı olarak izlenebilmekte ve verimlilik artırılabilmektedir. Otonom robotlar, manuel işgücüne olan ihtiyacı azaltarak inşaat sahasındaki görevlerin daha hızlı, verimli ve düzenli bir şekilde tamamlanmasını sağlamaktadır. Simülasyon teknolojisi, gerçek inşaat faaliyetleri sırasında ilerleyiş hakkında geri bildirimler sunarak iş süreçlerindeki verimliliği olumlu yönde etkilemektedir. Tüm yeniliklere rağmen inşaat sektörü hala büyük oranda insan gücü odaklı olarak süreçlerini devam ettirmektedir. Bu durum sektördeki verimlilik artışının sınırlı kalmasına ve dijital teknolojilerin sunduğu fırsatlardan tam anlamıyla yararlanılamamasına yol açmaktadır. Diğer sektörler dijital çağın sunduğu fırsatları üretim süreçlerine hızla uyarlayıp verimliliklerini artırırken inşaat sektörü bu ivmeyi yakalayamamıştır. Örneğin, otomotiv ve imalat sektörleri, dijitalleşme sayesinde üretim süreçlerinde büyük verimlilik artışları sağlamışlardır. Endüstri 4.0'ın yükselişiyle birlikte dijital teknolojilerin kullanımı ve entegrasyonu büyük ölçüde artmış ve iş süreçlerinde teknoloji odaklı dönüşümler yaşanmıştır. İnşaat sektörü de verimliliğini artırmak, kaynaklarını etkin bir şekilde kullanmak, iletişim sorunlarını gidermek ve zaman ve maliyet kayıplarını hafifletmek için dijital dönüşümü benimsenemelidir. Bu tez çalışmasında dijital dönüşümün Türk inşaat sektöründe benimsenmesi için etkili stratejileri belirlemek amaçlanmıştır. Bu amaçla öncelikle dijital dönüşümün doğru bir şekilde anlaşılması ve değerlendirilmesi için literatür araştırması yapılmıştır. Bu araştırma inşaat sektörü bağlamında sistematik bir literatür incelemesi yapabilmek için gereken teorik çerçevenin oluşturulmasını sağlamıştır. Ardından inşaat sektörü literatüründe dijital dönüşümün ele alınma biçimi araştırılmış; dijital dönüşümün tanımı, dijital teknolojiler ve inşaat sektöründeki uygulamaları, dijital dönüşümün inşaat sektörüne katkıları ve inşaat sektöründe dijital dönüşümü benimseme konuları incelenmiştir. Bu inceleme sonucunda inşaat sektörü literatüründe daha çok dijital teknolojiler ve teknolojilerin katkılarına odaklanıldığı, sektörde dijital dönüşümün benimsenmesini sağlayacak stratejilere değinen çalışmaların sınırlı olduğu tespit edilmiştir. Bu bulgu, dijital dönüşümün sektörde nasıl uygulanacağına dair somut stratejilerin belirlenmesi gerekliliğini ortaya koymuştur. Dijital dönüşümün katkılarının anlaşılması bu dönüşümün benimsenmesi yolunda teorik bir katkı sağlayarak ilk adımı oluştursa da; gerçek dönüşüm somut eylemleri gerektirir. Dolayısıyla dijital dönüşümün benimsenmesi ve uygulanması için stratejilerin belirlenmesi ve eyleme geçirilmesi gerektiği sonucuna varılmıştır. Türk inşaat sektörü için etkili dijital dönüşüm stratejilerinin belirlenmesi amacıyla yarı yapılandırılmış görüşmeler yapılmıştır. Bu görüşmeler Türk inşaat sektöründe dijital dönüşüm konusunda deneyim sahibi sektör profesyonelleri ile gerçekleştirilmiştir. Çevimiçi ve/veya yüzyüze metodlarla gerçekleştirilen görüşmelerde, literatür çalışmaları sonucunda elde edilen 16 dijital dönüşüm stratejisi Türk inşaat sektörü bağlamında değerlendirilmiştir. Katılımcılar stratejilerin Türk inşaat sektörünün dijital dönüşüm sürecine katkılarını aktarmış ardından dijital dönüşüme etki derecesini 1 (çok düşük) – 5 (çok yüksek) arasında puanlamıştır. Uzmanın strateji için 4 (yüksek) ya da 5 (çok yüksek) puan vermişse stratejiyi etkili bir şekilde uygulamak için öneri ve tavsiyelerde bulunmuştur. Yapılan görüşmeler ve değerlendirmeler sonucunda Türk inşaat sektörü için etkili dijital dönüşüm stratejileri standartların ve yasal çerçevenin oluşturulması, yukarıdan aşağıya bir yaklaşımın benimsenmesi, bütünleşik veritabanının oluşturulması, firmaların uzun vadeli ve kapsamlı dijital dönüşüm stratejilerini oluşturması, eğitim faaliyetlerinin düzenlenmesi, işbirlikçi ekiplerin geliştirilmesi ve veriye dayalı problem çözümü yapılması şeklinde belirlenmiştir. Elde edilen veriler Türk inşaat sektörünün dijital dönüşümünün başarılı bir şekilde gerçekleşmesi için hükümet ve organizasyon seviyelerinde yoğun olarak bazı düzenlemelerin yapılması ve veri ile ilgili konulara odaklanılması gerektiğini göstermiştir. Standartların ve yasal çerçevenin oluşturulması Türk inşaat sektörünün dijitalleşmesi noktasında en önemli itici güçtür. Bu standartlar, sektörde dijital teknolojilerin entegrasyonunu kolaylaştırarak bir uyum ve koordinasyon sağlamaya yardımcı olmaktadır. Yukarıdan aşağı bir yaklaşımla üst yönetimler ya da karar vericiler, organizasyonun dijital dönüşüm sürecinde hedef ve politikaları belirleyerek çalışanlarının motivasyonlarını artırarak dijitalleşme süreçlerini hızlandırabilmektedir. Bütünleşik veritabanı, veriye anlık olarak ulaşabilme imkanı sağlayarak iş süreçlerinde verimlilik artışını teşvik etmektedir. Projelerdeki veri yönetimini kolaylaştırarak bilgiye hızlı erişim ve doğru karar alma süreçlerini desteklemektedir. Organizasyonların uzun vadeli ve kapsamlı dijital dönüşüm stratejilerini oluşturması, sürecin belirli bir yol haritasıyla yönetilmesine yardımcı olmaktadır. Bu sayede firmalar dijital dönüşüm süreçlerinde karşılaşabilecekleri zorlukları önceden tespit ederek proaktif çözümler geliştirebilmektedir. Eğitim sayesinde çalışanlar sürecin kendilerine sağladıkları faydaları görebilmekte ve kendilerini teknik anlamda geliştirerek dönüşüm sürecine adapte olabilmektedir. İşbirlikçi ekiplerin geliştirilmesi, çeşitli disiplinlerin bir araya getirilerek yenilikçi çözüm üretme ve karmaşık sorunları daha etkili çözme potansiyelini artırmaya yardımcı olmaktadır. Veriye dayalı problem teşhisi, iş akışındaki sorunların hızla tespit edilmesini ve çözüm üretilmesini mümkün kılmaktadır. Bu çalışma sunduğu sonuçlarla, Türk inşaat sektöründe dijitalleşmeye adım atan ya da mevcut iş süreçlerini dijitalleştirmek isteyen paydaşlar ve firmalar için dijital dönüşüm yolculuklarında kılavuzluk edecek bir rehber niteliği taşımaktadır.